Two-dimensional and three-dimensional sliding window-based methods and systems for detecting vehicles

ABSTRACT

Provided is a method and system for efficient localization in still images. According to one exemplary method, a sliding window-based 2-D (Dimensional) space search is performed to detect a parked vehicle in a video frame acquired from a fixed parking occupancy video camera including a field of view associated with a parking region.

BACKGROUND

The present disclosure relates to a video-based method and system forefficient vehicle detection/localization in still images obtained from afixed video camera. The disclosed method and system are applicable toparking space management. However, it is to be appreciated that thepresent exemplary embodiments are also applicable to other likeapplications.

One challenge that parking management companies face while managingon-street parking is an accurate detection of available spaces.Conventional methods for detecting vehicle occupancy in parking spacesinclude non-video based sensing solutions. For example, “puck-style”sensors, shown in FIG. 1, typically use magnetometer readings to sensewhen a vehicle is detected in a parking space. Ultrasonic sensors, asshown in FIG. 2, operate by sending and receiving high frequency sonicwaves and evaluating a parking area based on processing a signal thathas been reflected back to the ultra-sonic sensor. The detectedinformation is wirelessly communicated to interested parties. Onedisadvantage associated with these sensor-based methods is a high costfor installation and maintenance of the sensors. In addition, themaintenance or replacement of a sensor may reduce parking efficiency ifa parking space is made unavailable for the service work.

Another method being explored is a video-based solution. This method isshown in FIG. 3 and includes monitoring on-street parking spaces usingnon-stereoscopic video cameras. The cameras output a binary signal to aprocessor, which uses the data for determining occupancies of theparking spaces.

One shortcoming of both technologies is that they are designed for, andlimited to, single-space parking configurations. On-street parking canbe provided in two different configurations. A first configuration isshown in FIG. 4 and includes single-space parking, also known asstall-based parking, in which each parking space is defined in a parkingarea by clear boundaries. The parking spaces are typically marked bylines (shown in phantom) that are painted on the road surface todesignate one parking space per vehicle. The second configuration isshown in FIG. 5 and includes multi-space parking, in which a longsection of street is designated as a parking area to accommodatemultiple vehicles. In this configuration, there are no pre-definedboundaries that designate individual parking stalls, so a vehicle canpark at any portion extending along the parking area. In many instances,the multi-space parking configurations are more efficient because, whenspaces are undesignated, drivers aim to fit more vehicles in amulti-space parking area having a same length as a single-space parkingarea.

At present, many departments of transportation are transitioning fromsingle-space parking configurations to the multi-space parkingconfigurations. Cities are eliminating parking meters and single-spaceparking configurations to reduce maintenance and other costs. Thesensor-based methods are best suited for parking areas where paintedlines typically demark a defined parking space for a single vehicle.However, an incorporation of the sensor-based methods for use inmulti-space parking configurations is conceptually difficult andexpensive to continue. Accordingly, this transition reduces a need forin-ground and other sensor-based methods.

Given the comparatively lower cost of a video surveillance camera, avideo-based solution offers a better value if it is incorporated into amanagement scheme for monitoring multi-space parking configurations, aswell as some applications of single-space street parking. Anotheradvantage of a video-based solution is that one video camera cantypically monitor and track several parking spots, whereas multiplesensors may be needed to reliably monitor one parking space in thesingle-space parking configuration. Additionally, maintenance of thevideo cameras is likely to be less disruptive than maintenance ofin-ground sensors.

INCORPORATION BY REFERENCE

-   U.S. Pat. No. 6,285,297, issued Sep. 4, 2001, by Jay H. Ball, and    entitled “Determining The Availability Of Parking Spaces”;-   U.S. application Ser. No. 13/836,310, filed Mar. 15, 2013, by Wu et    al., entitled “Methods and System for Automated In-Field    Hierarchical Training of a Vehicle Detection System”;-   U.S. application Ser. No. 13/441,294, filed Apr. 6, 2012, by Bernal    et al., entitled “Video-Based Detector And Notifier For Short-Term    Parking Violation Enforcement”;-   U.S. application Ser. No. 13/441,253, filed Apr. 6, 2012, by Bulan    et al., entitled “Video-Based System And Method For Detecting    Exclusion Zone Infractions”;-   U.S. application Ser. No. 13/441,269, filed Apr. 6, 2012, by Bulan    et al., and entitled “A System And Method For Available Parking    Space Estimation For Multispace On-Street Parking”;-   U.S. application Ser. No. 13/461,191, filed May 1, 2012, by Fan et    al., entitled “System And Method For Street-Parking-Vehicle    Identification Through License Plate Capturing”;-   U.S. application Ser. No. 13/684,817, filed Nov. 26, 2012, by Rong    et al., entitled “System And Method For Estimation Of Available    Parking Space Through Intersection Traffic Counting”;-   http://streetsmarttechnology.com, 2 pages, copyright 2011;-   http://www.alibaba.com/product-gs/373281312/Ultrasonic_Parking_Sensor.html,    3 pages, copyright 1999-2013;-   http://en.wikipedia.org/wiki/Hough_transform; 10 pages, Jan. 9,    2013;-   http://www.cs.brown.edu/˜pff/latent/, version 5, Sep. 5, 2012, 3    pages (http:people.cs.uchicago.edu/˜rbg/latent/);-   N. Dalal and B. Triggs “Histograms Of Oriented Gradients For Human    Detection”, in 2005, CVPR, 8 pages;-   T. Ojala, M. Pietikäinen, and D. Harwood, “A Comparative Study Of    Texture Measures With Classification Based On Feature    Distributions”, 1996 Pattern Recognition, volume 29, pages 51-59,    Department of Electrical Engineering, Oulu, Finland;-   M. Nilsson, J. Nordberg, and I. Claesson, “Face Detection Using    Local Smqt Features And Split Up Snow Classifier”, IEEE    International Conference on Acoustics, Speech, and Signal    Processing, 2007, Blekinge Institute of Technology, Ronneby, Sweden,    pages II-589 to II-592;-   F. Perronnin and C. Dance, “Fisher Kernels On Visual Vocabularies    For Image Categorization”, CVPR, 2007, Xerox Research Centre Europe,    Meylan, France, 8 pages;-   G. Csurka, C. Dance, J. Willamowski, L. Fan and C. Bray, “Visual    Categorization With Bags Of Keypoints”, ECCV SLCV, 2004, 16 pages;-   F. Perronnin, J. Sanchez and T. Mensink, “Improving The Fisher    Kernel For Large-Scale Image Classification”, ECCV, 2010, Xerox    Research Centre Europe, 14 pages;-   A. Neubeck and L. V. God, “Efficient Non-Maximum Suppression”, ICPR,    2006, Computer Vision Lab, Zurich, Switzerland, 6 pages;-   P. Felzenszwalb, R. Girshick, D. McAllester, D. Ramanan, “Object    Detection With Discriminatively Trained Part-Based Models,” IEEE    Transactions on Pattern Analysis and Machine Intelligence, Vol. 32,    No. 9, September 2010, pages 1627-1645; and-   Constantine Papageorgiou and Tomaso Poggio, “A Trainable System For    Object Detection”, International Journal of Computer Vision, 38(1),    pages 15-33, 2000, Netherlands; are incorporated herein by reference    in their entirety.

BRIEF DESCRIPTION

In one embodiment of this disclosure, described is a computerimplemented method of detecting a vehicle in a video frame, the videoframe acquired from a fixed parking occupancy video camera including afield of view associated with a vehicle parking region, the methodcomprising a) capturing a video frame from the fixed parking occupancyvideo camera, the video frame including a ROI (Region of Interest)oriented by an orientation angle relative to an orientation of an imageplane associated with the captured video frame, the ROI including one ormore parking spaces of the vehicle parking region; b) rotating the ROIby the orientation angle of the ROI; and c) performing a slidingwindow-based 2-D (Dimensional) space search for a vehicle within therotated ROI, the sliding window-based 2-D space search extracting one ormore features associated with each of a plurality of windows andaccessing a classifier to classify each window as including a vehicle ornot including a vehicle.

In another embodiment of this disclosure, described is a vehicledetection system associated with a vehicle parking region, the vehicledetection system comprising a parking occupancy video camera directedtowards the vehicle parking region; and a controller operativelyassociated with the parking occupancy video camera, the controllerconfigured to execute computer instructions to perform a process ofdetecting a vehicle in a video frame including a) capturing a videoframe from the parking occupancy video camera, the video frame includinga ROI (Region of Interest) orientated by an orientation angle, relativeto an orientation of an image plane associated with the captured videoframe, the ROI including one or more parking spaces of the vehicleparking region; b) rotating the ROI by the orientation angle of the ROI;and c) performing a sliding window-based 2-D (Dimensional) space searchfor a vehicle within the rotated ROI, the sliding window-based 2-D spacesearch extracting one or more features associated with each of aplurality of windows and accessing a classifier to classify each windowas including a vehicle or not including a vehicle.

In still another embodiment of this disclosure, described is a computerimplemented method of determining parking occupancy associated with avehicle parking region comprising a) capturing a video frame from aparking occupancy video camera directed towards the vehicle parkingregion; b) rotating the captured video frame by a predeterminedorientation angle of a longitudinal axis associated with a ROI (Regionof Interest) associated with the captured video frame, relative to anorientation of an axis associated with an image plane associated withthe captured video frame, the ROI including one or more parking spacesof the parking region; and c) performing a sliding window-based 2-D(Dimensional) space search for a vehicle within the ROI associated withthe rotated captured video frame, the sliding window-based 2-D spacesearch extracting one or more features associated with each of aplurality of windows and accessing a classifier to classify each windowas including a vehicle or not including a vehicle.

In yet another embodiment of this disclosure, described is a computerimplemented method of detecting a vehicle in a video frame, the videoframe acquired from a fixed parking occupancy video camera including afield of view associated with a vehicle parking region, the methodcomprising a) capturing a video frame from the fixed parking occupancyvideo camera, the video frame including a ROI (Region of Interest)oriented by an orientation angle relative to an orientation of an imageplane associated with the captured video frame, the ROI including one ormore parking spaces of the vehicle parking region; and b) performing asliding window-based 2-D (Dimensional) Space Search for a vehicle withthe ROI, the sliding window-based 2-D space search extracting one ormore features associated with each of a plurality of windows andaccessing a classifier to classify each window as including a vehicle ornot including a vehicle, wherein step b) performs the slidingwindow-based 2-D space search along an orientation axis of the ROIassociated with the orientation angle of the ROI relative to theorientation of the image plane.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a “puck-style” sensor-based method for detecting parkingspace occupancy according to the PRIOR ART.

FIG. 2 shows an ultrasonic sensor-based method for detecting parkingspace occupancy according to the PRIOR ART.

FIG. 3 shows a video-based method for detecting parking space occupancyaccording to the PRIOR ART.

FIG. 4 shows a single-space parking configuration.

FIG. 5 show a multiple-space parking configuration.

FIG. 6 illustrates a sliding window search for object detection, thesearch space being 4 dimensional, i.e. x-direction, y-direction, windowheight and window width.

FIG. 7 illustrates a captured video frame including vehicles ofdifferent sizes in the field of view of a parking occupancy cameraassociated with a city block.

FIG. 8 is a flow chart of an offline training method associated with avehicle detection method according to an exemplary embodiment of thisdisclosure.

FIG. 9 is a set of positive samples extracted from a region of interestaccording to an exemplary embodiment of this disclosure.

FIG. 10 is a set of negative samples extracted from a region of interestaccording to an exemplary embodiment of this disclosure.

FIG. 11 is a flow chart of an online vehicle detection method accordingto an exemplary embodiment of this disclosure.

FIGS. 12, 13 and 14 illustrate a sliding window search in 2-D in aregion of interest according to an exemplary embodiment of thisdisclosure.

FIG. 15 is a sample captured video frame illustrating a field of view ofa camera installed on a city block according to an exemplary embodimentof this disclosure.

FIG. 16 illustrates a sliding window search along a parallelogram in aregion of interest according to an exemplary embodiment of thisdisclosure.

FIG. 17 is one example of a vehicle detection system configurationaccording to an exemplary embodiment of this disclosure.

FIG. 18 is a block diagram of a vehicle detection system according to anexemplary embodiment of this disclosure.

DETAILED DESCRIPTION

This disclosure provides a computationally efficient vision-basedvehicle detection method and system from images acquired with fixedcameras operatively associated with a parking occupancy managementsystem. As shown in FIG. 6, object localization algorithms typicallyimplement a 4-D (Dimensional) sliding window search approach in whichboth the location (x and y) and the size (height and width) of thewindow are changed, and where the candidate window(s) yielding the bestclassification score are kept. This is a computationally expensiveapproach because optimization is performed on a four-dimensional (4-D)space. This disclosure provides improved efficiency by exploiting apriori knowledge of a camera and scene geometry, and searching across areduced feasible candidate set, or region of interest (ROI). Since theheight of an ROI is known, which coincides with the height of a feasibleparking region, searches only need to be performed across horizontallocations within the ROI and across an object length, i.e. vehicle,length. A mapping of the ROI to rectangular coordinates is achieved byrotating the ROI by the angle of a longitudinal axis associated with theROI, such as but not limited to a curb relative a reference image planeassociated with the captured video. Benefits of the disclosedmethod/systems include an increase in computational efficiency with noloss in performance. In addition, the disclosed vehicle detection methodand system is robust to variations in the way an ROI iscropped/selected. According to one exemplary embodiment, manualintervention is used for each camera deployed, as the ROI is specific tothe geometry of the camera. However, as disclosed in copending patentapplication Ser. No. 13/836,310, filed Mar. 15, 2013, entitled “Methodsand System for Automated In-Field Hierarchical Training of a VehicleDetection System”, by Wu et al., an automatic hierarchical approachprovides a manner of automatically deploying multiple camera systems,whereby no manual intervention, or minimal manual intervention isrequired.

Video-based parking occupancy detection systems have been proposed inthe literature to provide real-time parking occupancy data to drivers toreduce fuel consumption and traffic congestion, see U.S. Pat. No.6,285,297 by Jay H. Ball, issued Sep. 4, 2001, entitled “Determining theavailability of parking spaces” and U.S. application Ser. No.13/441,269, Filed Apr. 6, 2012, by Bulan et al., entitled “A System andMethod For Available Parking Space Estimation For Multispace On-StreetParking”. An important step in these video-based parking systems islocalizing a parked vehicle within a region of interest as quickly aspossible to enable real-time processing for the overall system. Asbriefly discussed above, existing search algorithms for the purpose ofobject detection, i.e. vehicle detection, typically perform a slidingwindow search in 4-D space, i.e., x-direction, y-direction, and windowsize (height and width) as shown in FIG. 6, and are relativelyinefficient for specialized applications where the video cameras arefixed. In the FIG. 6, the object of interest is a dog and a fixed sizewindow is sliding across the image, i.e. along x and y directions. Thissearch will locate dogs that fit in the selected window size such as theone at the right lower corner. In order to locate all dogs of differentsizes in the image, the search is repeated for various window sizes, byvarying the window height and width. Alternatively, the search can beperformed with a fixed window size on sub-sampled images to account fordifferent object sizes. In any case, this search process iscomputationally expensive as the search space for localizing an objectis 4-dimensional, i.e., x-direction, y-direction, sliding window heightand window width. This process can be especially cumbersome for locatingvehicles in a video-based parking occupancy detection system as the sizeof vehicles in a region of interest associated with the parking regionmay vary. This disclosure provides a fast and efficient method/systemfor localization of parked vehicles within a region of interest in astill image/video frame from a fixed camera. Unlike the conventionalsearch algorithms for object detection, the provided method and systemperforms vehicle searching in 2-D space instead of 4-D, oralternatively, in a 3-D space.

According to an exemplary method, fast and efficient localization ofvehicles within a region of interest (ROI) associated with an on-street,i.e. curb parking region. A still image/video frame including theparking region is provided where a camera and ROI within a field of viewassociated with the camera are fixed. The method includes two phases: 1)offline (training) phase, and 2) online phase. In one exemplaryembodiment of the disclosed method, the offline (training) phaseincludes, a) Capturing video from a parking occupancy camera; b)Defining an ROI corresponding to the parking region in the capturedvideo; c) Determining the orientation angle of the parking regionrelative to an image plane associated with the captured video androtating the captured image by the determined orientation angle; d)Carving out the ROI in the rotated image plane and extracting positivesamples (vehicle) and negative samples (nonvehicle) from the carved outregion; and e) Extracting a set of features from the positive andnegative samples and training a classifier using the extracted featuresand class labels for each sample. During the online phase of theembodiment, the following steps are performed; f) Acquiring animage/video frame from the camera; g) Rotating the acquired image by theorientation angle associated with the orientation angle determined inthe offline phase; h) Carving out the ROI in the rotated image plane; i)Performing a window-based search within the carved out region along thex-direction at various window widths, i.e. 2-D sliding window search; j)Extracting one or more features for each window and inputting theextracted features into the trained classifier generated during theoffline phase to classify each window as “vehicle” or “nonvehicle”; andk) Suppressing overlapping “vehicle” windows that correspond to the samevehicle.

It is to be understood that while the disclosed embodiments primarilydescribe rotating a ROI/captured image associated with a parking region,alternatively, 2-D searching may be performed along an axis parallel tothe orientation of the ROI/captured image relative to the image plane,without rotating the ROI/captured image.

FIG. 7 shows field of view of a camera installed on a city block. AsFIG. 7 shows, the size, i.e., height and width, and aspect ratio of thevehicles 710, 715, 720 in the parking region varies significantly.Notably, the reason for different size of vehicles on the image plane705 is twofold: 1) Physical sizes of different types of vehicles, i.e.,height and length, vary (e.g., motorcycle vs. truck). In addition, 2)objects that are closer to a fixed camera appear larger on the imageplan 705. In order to locate all vehicles of different sizes in aparking region, conventional sliding-window based object detectionalgorithms require searching all windows from a smallest size to alargest size, i.e., by varying height and width of search window, alongx and y directions in a determined region of interest, which iscomputationally expensive.

For a video-based parking occupancy application, the processing time ofthe overall system should be real-time to provide relevant parking datato inform drivers of the parking occupancy status of a particularparking region. Importantly, vehicle localization may be a single modulewithin an entire video-processing algorithm and vehicle localizationconstitutes the computationally most expensive portion of the overallprocessing of the system. It is, therefore, highly desirable to performvehicle localization in a fast and efficient manner to enable real-timeprocessing for the overall system.

As briefly discussed above, the method and system disclosed herein iscomposed of two phases, 1) offline (training) phase, and 2) onlinephase. Described now are further details of the vehicle detection methodand system to facilitate a better understanding.

According to one exemplary embodiment of the disclosed method, theoffline (training) phase includes the following steps:

a) Capture video from a parking occupancy camera;

b) Define a region of interest corresponding to parking region in thecaptured video;

c) Determine the orientation angle of the parking region on the imageplane and rotating the image with the angle determined;

d) Carve out the region of interest in the rotated image plane andextracting positive samples (vehicle) and negative samples (nonvehicle)from the carved out region; and

e) Extract a set of features from positive and negative samples andtraining a classifier using the extracted features and the class labelsfor each sample.

The offline process may be performed at the camera installation/set-up.Once the video cameras are set-up and the offline processing iscompleted, vehicles can be localized in each frame using the trainedclassifier. This is what is referred to as the online phase. During theonline phase of the exemplary embodiment, the following steps areperformed:

f) Acquire an image/video frame from the camera;

g) Rotate the image with the angle determined in the offline phase;

h) Carve out the region of interest in the rotated image plane;

i) Perform a window-based search within the carved out region alongx-direction for various window width;

j) Extract a set of features for each window and feeding them into theclassifier to classify each window as “vehicle” or “nonvehicle”; and

k) Suppress overlapping “vehicle” windows that correspond to the samevehicle.

Details of the Offline (Training) Phase.

FIG. 8 shows a high level overview of the offline (training) phaseillustrating the individual steps performed. Described below is eachstep in further detail.

a) Capture video from a parking occupancy camera, 805-810.

One or more video frames are captured from a fixed camera that monitorsa parking region of interest. The length of the captured video dependson the level of activity in the parking region to gather enough trainingsamples. For low activity parking regions, video acquired over arelatively longer time frame may be required. If monitoring at night isdesired, NIR (near infrared) cameras may be beneficial, and/or the useof external illumination sources. Relatively inexpensive NIR cameras arereadily available, as the low-end portion of the near-infrared spectrum,i.e. 700 nm-1000 nm, can be captured with equipment that is designed tocapture visible light.

b) Defining a region of interest (ROI) corresponding to the parkingregion in the captured video, 815.

The location of the parking region on the image plane associated withthe captured video depends on the geometric configuration of the cameraset-up and can be specified manually at camera set-up/installation. Forexample, the parking region may be specified by a polygon, an ellipse orany other closed geometric shape. The region of interest within theimage plane may be defined based on a typical passenger car size, wherethe ROI includes complete coverage or partial coverage of the typicalpassenger car in the parking region, while not including much of thebackground region nor including a vehicle when a vehicle is parked inthe region.

c) Determine the orientation angle of the parking region relative to theimage plane and rotating the captured image by the orientation angledetermined, 815.

Once the parking region is defined with respect to the image plane, theorientation angle of the parking region can be defined by specifying twopoints in the parking region as the start and end points. These twopoints, for example, can be the left-bottom and right-bottom points ofthe region of interest, i.e. parking region, on the image plane. Theorientation angle from these points may be calculated as:

$\theta = {{atan}\left( \frac{y_{2} - y_{1}}{x_{2} - x_{1}} \right)}$

where θ is the orientation angle of the parking region on the imageplane and (x₁,y₁) and (x₂,y₂) are the coordinate locations of the startand end points of the parking region, respectively. Alternatively, theorientation angle of the parking region may be determined from a curbangle associated with the captured video. Curb angle may be estimatedautomatically by, for example, finding the longest line in the region ofinterest using an algorithm like a Hough transform on the edge map of astatic scene, see http://en.wikipedia.org/wiki/Hough_transform, andcalculating the angle of this line. This line detection can preferablybe performed using multiple frames at time instants when there is nomotion detected in the scene. Another way to estimate curb angle is byusing an activity map that shows the regions where motion occurs themost. The most active region in the activity map will be oriented in thedirection of the traffic which typically runs parallel to the curb.

After the orientation of the parking region is determined, theimage/video frame, 805-810, is rotated by the determined orientationangle. The rotated image/video frame aligns the region of interest alongthe horizontal direction as shown in FIGS. 8, 820 and 825.

d) Carve out the region of interest in the rotated image frame andextract positive samples (vehicle) and negative samples (nonvehicle)from the carved out region, i.e. ROI, 830.

After the captured image is rotated, the region of interest is carvedout from the image. Positive (vehicle) 835 and negative (nonvehicle) 840samples are then extracted from the carved out region, i.e., parkingregion. These samples may be extracted either manually from the carvedout region or automatically using a hierarchical training approach asdescribed in copending patent application by Wu et al. “AutomatedIn-Field Hierarchical Training of Vehicle Detector For GeneralDeployment of Vehicle Detection Cameras.

FIGS. 9 and 10 show a set of positive 1005, 1010, 1015, 1020, 1025 andnegative 905, 910, 915, 920, 925 samples extracted from the carved outregion, respectively. Note that in step b of the offline phase, theregion of interest, i.e., parking region, on the image plane istypically defined by the size of a passenger car to cover it fully inthe region. This ensures that when a positive sample is extracted fromthe carved out region, it mostly includes car video in the extractedpatch of video rather than the background of the video, i.e. buildings,pedestrians, etc. In the event a large vehicle, e.g., a truck, parks inthe region, a portion of the vehicle, i.e. the upper portion, may becropped out of the region of interest as shown in FIG. 9. This however,is not an issue for parking occupancy detection applications accordingto this disclosure because vehicle height is not a concern for theexpected vehicle profiles.

In contrast to conventional vision-based object detection, i.e. vehicledetection, methods where training samples contain the entire object withlittle or no background, the disclosed method includes variable amountsof background depending on the region of interest, the vehicle size, andits location on the street. In essence, the classifier is trained torecognize/detect vehicles through a pre-defined mask with a distortedcamera view. It is this variant that enables reducing a typical 4-Dsearch (x-direction, y-direction, object height, object length) to a 2-Dsearch (x-direction and object length). Furthermore, since parkedvehicles tend to have consistent relative positions relative to thestreet curb, the disclosed method does not suffer from accuracydegradation by including such variable amounts of background.

e) Extract a set of features from positive and negative samples andtraining a classifier using the extracted features and the class labelsfor each sample, 845.

After a sufficient set of positive samples and negative samples arecollected, a classifier may be trained using a manual and/or a machinelearning approach. The machine learning approach is achieved byinitially extracting a set of features from the collected samples. Thesefeatures may be extracted in a variety of ways including the following:

-   -   Dimensionality reduction techniques such as principal component        analysis (PCA) or linear discriminant analysis (LDA).    -   Texture based features such as histogram of oriented gradients        (HOG), see N. Dalai and B. Triggs “Histograms of Oriented        Gradients for Human Detection”, in 2005 CVPR, local binary        patterns (LBP) and T. Ojala, M. Pietikäinen, and D. Harwood, “A        Comparative Study of Texture Measures with Classification Based        on Feature Distributions”, 1996 Pattern Recognition, vol. 29,        pp. 51-59, or successive mean quantization transform features        (SMQT), see M. Nilsson, J. Nordberg, and I. Claesson, “Face        Detection using local SMQT Features and Split Up SNoW        Classifier”, IEEE International Conference on Acoustics, Speech,        and Signal Processing, 2007.    -   Features based on color attributes (e.g., color histogram).    -   Scale invariant features such as scale invariant feature        transform (SIFT) features or speeded up robust features (SURF).    -   Local image descriptors such as bag of visual words (BOW),        see G. Csurka, C. Dance, J. Willamowski, L. Fan and C. Bray,        “Visual categorization with bags of keypoints”, ECCV SLCV, 2004,        or Fisher vectors, see F. Perronnin and C. Dance, “Fisher        kernels on visual vocabularies for image categorization”, CVPR,        2007 and F. Perronnin, J. Sanchez and T. Mensink, “Improving the        Fisher kernel for large-scale image classification”, ECCV, 2010.

Any combination of the features listed above may be used to provide afinal feature vector. A linear/non-linear classifier, such as a linearSupport Vector Machine (SVM), may then be trained by feeding thefeatures extracted from the set of training samples and the classlabels, i.e. vehicle and non-vehicle, of the training samples, i.e. anindication of positive or negative for each training sample, to theclassifier.

It is to be understood that while the disclosed embodiments use a twoclass classifier, i.e. vehicle and non-vehicle, it is within the scopeof this disclosure to train a classifier with three or moreclassifications, such as type 1 vehicle, type 2 vehicle, non-vehicle,etc., where a plurality of “positive” sample types would be used, andpossibly a plurality of “negative” sample types.

Details of the Online Phase.

FIG. 11 shows a high level overview of the online phase. Described belowis each step in further detail.

f) Acquire an image/video frame 1105 from the video camera.

Once the offline processing is completed, the online phase begins byacquiring images/video frames from the same camera used to monitor theregion of interest for the offline phase.

g) Rotate the acquired image by the orientation angle determined in theoffline phase, 1110.

The Acquired image 1105 is rotated by the orientation angle determinedin the offline phase.

h) Carve out the region of interest in the rotated image plane.

After rotating the image, the region of interest is carved out from therotated image as shown in FIG. 11, 1112.

i) Perform a window-based search within the carved out region along thex-direction for various window widths, 1115.

A sliding window search is performed within the carved out region. Theadvantage(s) of the disclosed method and system is provided by theperformance of this step because the sliding window search 1117 is onlyperformed in 2-D, i.e. along the x-direction by varying the windowwidth, as compared to a 4-D sliding window search as previouslydescribed. FIGS. 12, 13 and 14 show exemplary embodiments of a slidingwindow search in 2-D where a fixed window slides along the horizontaldirection. The search is repeated for different window widths to detectvehicles of different length. Note that window height is fixed and thewindow search is performed only along the x-direction, which enables thesearch to be performed in 2-D, rather than the conventional 4-D searchwhich is computationally more expensive.

Stated another way, given an M×N image I(x,y)xε[1, M] & yε[1, N], acropped image I_(c)(x,y) of a typical rectangular search-window forobject detection can be specified by 4-parameters (x₀, y₀, w, h), where,a sub-image is a cropped version of I(x,y), i.e.I _(c)(x,y)=I(x,y) for all xε[x ₀ ,x ₀ +w] & yε[y ₀ ,y ₀ +h].

For a 4-D sliding window search, all four parameters (x₀, y₀, w, h)would be varied. That is, according to a 4-D sliding window search, asearch will be performed, for example, for:

x₀=1, 3, 5, . . . , M—w, for every fixed (y₀, w, h), i.e. slide thewindow at a step of 2 pixels in x-direction.

y₀=1, 3, 5, . . . , N—h, for every fixed (x₀, w, h), i.e. slide thewindow at a step of 2 pixels in y-direction.

w=40, 50, 60, . . . , for every fixed (x₀, y₀, h), i.e. with variouswidths of the sliding window at the value of 40, 50, 60, . . . .

h=20, 30, 40, . . . , for every fixed (x₀, y₀, w), i.e. with variousheights of the sliding window at the value of 20, 30, 40, . . . .

For a 2-D sliding window (illustrated in FIGS. 12 and 13), searching isonly done for various (x₀, w), while keeping the height of therectangular mask h and y₀, the y-position of the upper-left corner ofthe rectangular mask, constant.

FIG. 12 illustrates one example of a first iteration of a 2-D slidingwindow approach including a window size of W_(w1) width and W_(h)height. The window incrementally slides from position W₁ to positionW_(n).

FIG. 13 illustrates one example of a second iteration of the 2-D slidingwindow approach including a window size W_(w2) width and W_(h) height,the window incrementally slides from position W₁ to position W_(n),where W₁-W_(n) may or may not be the same W₁-W_(n) positions of FIG. 12.

Notably, FIG. 12 and FIG. 13 illustrate a 2-D sliding window approachaccording to exemplary embodiments of this disclosure where a fixedcamera is directed substantially perpendicular to the longitudinal axisassociated with the ROI 1205. In contrast, FIG. 14 illustrates thesituation where the fixed camera is directed towards the ROI at anon-perpendicular angle, where the height of vehicles located to theright of the camera is relatively smaller than the height of vehiclesnear the left of the camera. For the geometric configuration of FIG. 14,a 2-D sliding window search is performed by varying the aspect ratio ofthe sliding windows W₁-W_(n), thereby generating windows of heightsW_(n1)-W_(n2). Importantly, the search is still performed along a fixedlongitudinal axis, unlike a conventional 4-D sliding window approach.

While the focus of this disclosure is directed to a 2-D sliding windowvehicle detection method/system, alternatively, a 3-D sliding windowapproach is within the scope of the disclosure. While a 3-D slidingwindow approach is computationally more expensive than a 2-D approach, a3-D approach is computationally less expensive than a conventional 4-Dapproach.

One example of a 3-D sliding window vehicle detection method/systemincludes the geometric configuration of FIG. 14, where the window heightand width are varied as the position of the sliding window isincremented along a fixed longitudinal axis associated with the ROI.

j) Extracting a set of features for each window and inputting them tothe classifier trained in the offline phase to classify each window.

The same set of features is extracted for each search window asdescribed in step e) of the offline phase. The extracted features ofthis step are then inputted to the trained classifier to classify eachwindow into one of “vehicle” or “nonvehicle” categories.

k) Suppressing overlapping “vehicle” windows that correspond to the samevehicle.

In most cases, the classifier will detect more than one windowclassified as a “vehicle” for the same vehicle. This is illustrated inFIG. 11 where three windows are detected as “vehicle” for the vehicle inscene 1117. In order to acquire the window that best fits the vehicle1122, a well-known method in the literature called “non-maximasuppression” 1120 may be performed on the detected windows. See A.Neubeck and L. V. Gool, “Efficient Non-Maximum Suppression”, ICPR, 2006.

In an instantiation of non-maxima suppression, the suppression may beperformed based on classification scores. For example, all windows thatoverlap more than 50% with a higher score window are suppressed.

Note that the provided method and system of this disclosure reduces thesearch space from 4-D to 2-D and hence, can achieve faster processingcompared to conventional sliding window search in 4-D space. Providedbelow is the detection performance according to an experiment of theprovided method. The performance data provided demonstrated themethod/system does not compromise detection performance in favor offaster processing. Initially, six days of video of a city block wererecorded from 7:30 am to 7:00 pm. The recorded videos had a frame of 5frames per second (fps) with a resolution of 640×512. FIG. 15 shows thefield of view of the camera used to capture the video.

From the captured videos, the parking region was defined on the imageplane and the rotation angle was determined from the orientation of theparking region relative to the image plane. The rotation angle wascalculated as degrees. From six days of captured video, four of the dayswere used for the training phase. Training videos were manually reviewedand vehicles distinctly parked were cropped as positive samples fortraining. For negative samples, about 500 samples were randomlygenerated by using video processing to get low activity regions orframes, and cropping the background at random positions with sizessimilar to the size of a cropped vehicle. From these positive andnegative samples, HOG features were calculated. The calculated features,along with the class labels for each sample, were then used to train alinear SVM classifier.

This system was then tested on videos from 2 days of the originallyacquired video that were not part of the training days video. Table 1shows the size of the training set for this manual process and theresulting performance on the 2-day test videos. The disclosed method andsystem missed only one out of 75 vehicles without detecting any falsepositive. Note that for this experiment, the vehicle detectionperformance is directly assessed by running it independent of othervideo & image processing steps in the overall system so that the resultsare not confounded by other processing in the entire vision-basedparking management system. The assessment was done by running thevehicle detection system on still images extracted from the test videoevery 20 minutes.

TABLE 1 Detection performance of the disclosed method/system Testingperformance Training Set (4-day videos) (2-day videos) No. of positivesamples 407 True positive 74/75 (manual crop) (98.7%) No. of negativesamples 482 False positive  0/75 (random crop)   (0%)

Provided herein is a method and system for fast localization of vehiclesin images/video frames from fixed cameras. Notably, the method andsystem are described through a specific embodiment to facilitate theteaching of the method and system but the method can be realized inother ways which are within the scope of this disclosure.

In another embodiment, for example, the rotation step can be bypassedand a parallelogram can be used to define the region of interest on theimage plane. In this case, search windows are parallelograms and theyslide across the region of interest as shown in FIG. 16. Note that thesechanges should apply to both offline and online phases to realize theembodiment. However, the additional rotation step helps normalize thecamera views across different camera installation sites and thus enableus to deploy our method to various on-street parking sites moreeffectively, see Wu et al. “Automated In-Field Hierarchical Training ofVehicle Detector For General Deployment of Vehicle Detection Cameras”.Hence it is preferred to have this step from the perspective of generaldeployments.

The advantage of the provided method is more pronounced when theimage/video resolution gets higher to enable additional functionalities(e.g., license plate recognition) for law enforcement or parking paymentmanagement. Notable, the 4-D search space for the classical approachwill be relatively larger than the 2-D search space and, consequently,the processing time will lag behind the real-time requirement of theoverall system.

With reference to FIG. 17, illustrated is a diagram of the geometricconfiguration associated with one exemplary embodiment of a vehicledetection system according to this disclosure. The vehicle detectionsystem includes a fixed video camera 1717 and a parking region, i.e.ROI, 1710. The parking region includes parked vehicles 1712 and 1714,but may include more or less parked vehicles.

FIG. 18 is a schematic illustration of a parking space determinationsystem 1800 according to one exemplary embodiment, the system includinga vehicle detection system as described herein. The system includes adetermination device 1802, an image capture device 1804, and a storagedevice 1806, which may be linked together by communication links,referred to herein as a network. In one embodiment, the system 1800 maybe in further communication with a user device 1808. These componentsare described in greater detail below.

The determination device 1802 illustrated in FIG. 18 includes acontroller that is part of or associated with the determination device1802. The exemplary controller is adapted for controlling an analysis ofvideo data received by the system 1800. The controller includes aprocessor 1810, which controls the overall operation of thedetermination device 1802 by execution of processing instructions thatare stored in memory 1814 connected to the processor 1810.

The memory 1814 may represent any type of tangible computer readablemedium such as random access memory (RAM), read only memory (ROM),magnetic disk or tape, optical disk, flash memory, or holographicmemory. In one embodiment, the memory 1814 comprises a combination ofrandom access memory and read only memory. The digital processor 1810can be variously embodied, such as by a single-core processor, adual-core processor (or more generally by a multiple-core processor), adigital processor and cooperating math coprocessor, a digitalcontroller, or the like. The digital processor, in addition tocontrolling the operation of the determination device 1802, executesinstructions stored in memory 1814 for performing the parts of a methoddiscussed herein. In some embodiments, the processor 1810 and memory1814 may be combined in a single chip.

The determination device 1802 may be embodied in a networked device,such as the image capture device 1804, although it is also contemplatedthat the determination device 1802 may be located elsewhere on a networkto which the system 1800 is connected, such as on a central server, anetworked computer, or the like, or distributed throughout the networkor otherwise accessible thereto. The video data analysis, i.e. vehicledetection, phases disclosed herein are performed by the processor 1810according to the instructions contained in the memory 1814. Inparticular, the memory 1814 stores a video capture module 1816, whichcaptures video data of a parking area of interest; an initializationmodule 1818, which initializes the system; and a stationary vehicledetection module 1820, which detects vehicles that are in the parkingarea of interest; a classification module 1822, which classify whether aROI includes a vehicle parked in the area of interest. Embodiments arecontemplated wherein these instructions can be stored in a single moduleor as multiple modules embodied in the different devices.

The software modules as used herein, are intended to encompass anycollection or set of instructions executable by the determination device1802 or other digital system so as to configure the computer or otherdigital system to perform the task that is the intent of the software.The term “software” as used herein is intended to encompass suchinstructions stored in storage medium such as RAM, a hard disk, opticaldisk, or so forth, and is also intended to encompass so-called“firmware” that is software stored on a ROM or so forth. Such softwaremay be organized in various ways, and may include software componentsorganized as libraries, Internet-based programs stored on a remoteserver or so forth, source code, interpretive code, object code,directly executable code, and so forth. It is contemplated that thesoftware may invoke system-level code or calls to other softwareresiding on a server (not shown) or other location to perform certainfunctions. The various components of the determination device 1802 maybe all connected by a bus 1828.

With continued reference to FIG. 18, the determination device 1802 alsoincludes one or more communication interfaces 1830, such as networkinterfaces, for communicating with external devices. The communicationinterfaces 1830 may include, for example, a modem, a router, a cable,and/or Ethernet port, etc. The communication interfaces 1830 are adaptedto receive video and/or image data 1832 as input.

The determination device 1802 may include one or more special purpose orgeneral purpose computing devices, such as a server computer or digitalfront end (DFE), or any other computing device capable of executinginstructions for performing the exemplary method.

FIG. 18 further illustrates the determination device 1802 connected toan image source 1804 for inputting and/or receiving the video dataand/or image data (hereinafter collectively referred to as “video data”)in electronic format. The image source 1804 may include an image capturedevice, such as a camera. The image source 1804 can include one or moresurveillance cameras that capture video data from the parking area ofinterest. For performing the method at night in parking areas withoutexternal sources of illumination, the cameras 1804 can include nearinfrared (NIR) capabilities at the low-end portion of a near-infraredspectrum (700 nm-1000 nm).

In one embodiment, the image source 1804 can be a device adapted torelay and/or transmit the video captured by the camera to thedetermination device 1802. For example, the image source 1804 caninclude a scanner, a computer, or the like. In another embodiment, thevideo data 1832 may be input from any suitable source, such as aworkstation, a database, a memory storage device, such as a disk, or thelike. The image source 1804 is in communication with the controllercontaining the processor 1810 and memories 1814.

With continued reference to FIG. 18, the system 1800 includes a storagedevice 1806 that is part of or in communication with the determinationdevice 1802. In a contemplated embodiment, the determination device 1802can be in communication with a server (not shown) that includes aprocessing device and memory, such as storage device 1806.

With continued reference to FIG. 18, the video data 1832 undergoesprocessing by the determination device 1802 to output a determination1838 regarding parking space availability to an operator in a suitableform on a graphic user interface (GUI) 1840 or to a user device 1808,such as a smart phone belonging to a driver in transit or to vehiclecomputer and/or GPS system, that is in communication with thedetermination device 1802. The GUI 1840 can include a display, fordisplaying information, such as the parking space availability anddimension, to users, and a user input device, such as a keyboard ortouch or writable screen, for receiving instructions as input, and/or acursor control device, such as a mouse, trackball, or the like, forcommunicating user input information and command selections to theprocessor 1810.

Some portions of the detailed description herein are presented in termsof algorithms and symbolic representations of operations on data bitsperformed by conventional computer components, including a centralprocessing unit (CPU), memory storage devices for the CPU, and connecteddisplay devices. These algorithmic descriptions and representations arethe means used by those skilled in the data processing arts to mosteffectively convey the substance of their work to others skilled in theart. An algorithm is generally perceived as a self-consistent sequenceof steps leading to a desired result. The steps are those requiringphysical manipulations of physical quantities. Usually, though notnecessarily, these quantities take the form of electrical or magneticsignals capable of being stored, transferred, combined, compared, andotherwise manipulated. It has proven convenient at times, principallyfor reasons of common usage, to refer to these signals as bits, values,elements, symbols, characters, terms, numbers, or the like.

It should be understood, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. Unlessspecifically stated otherwise, as apparent from the discussion herein,it is appreciated that throughout the description, discussions utilizingterms such as “processing” or “computing” or “calculating” or“determining” or “displaying” or the like, refer to the action andprocesses of a computer system, or similar electronic computing device,that manipulates and transforms data represented as physical(electronic) quantities within the computer system's registers andmemories into other data similarly represented as physical quantitieswithin the computer system memories or registers or other suchinformation storage, transmission or display devices.

The exemplary embodiment also relates to an apparatus for performing theoperations discussed herein. This apparatus may be specially constructedfor the required purposes, or it may comprise a general-purpose computerselectively activated or reconfigured by a computer program stored inthe computer. Such a computer program may be stored in a computerreadable storage medium, such as, but is not limited to, any type ofdisk including floppy disks, optical disks, CD-ROMs, andmagnetic-optical disks, read-only memories (ROMs), random accessmemories (RAMs), EPROMs, EEPROMs, magnetic or optical cards, or any typeof media suitable for storing electronic instructions, and each coupledto a computer system bus.

The algorithms and displays presented herein are not inherently relatedto any particular computer or other apparatus. Various general-purposesystems may be used with programs in accordance with the teachingsherein, or it may prove convenient to construct more specializedapparatus to perform the methods described herein. The structure for avariety of these systems is apparent from the description above. Inaddition, the exemplary embodiment is not described with reference toany particular programming language. It will be appreciated that avariety of programming languages may be used to implement the teachingsof the exemplary embodiment as described herein.

A machine-readable medium includes any mechanism for storing ortransmitting information in a form readable by a machine (e.g., acomputer). For instance, a machine-readable medium includes read onlymemory (“ROM”); random access memory (“RAM”); magnetic disk storagemedia; optical storage media; flash memory devices; and electrical,optical, acoustical or other form of propagated signals (e.g., carrierwaves, infrared signals, digital signals, etc.), just to mention a fewexamples.

The methods illustrated throughout the specification, may be implementedin a computer program product that may be executed on a computer. Thecomputer program product may comprise a non-transitory computer-readablerecording medium on which a control program is recorded, such as a disk,hard drive, or the like. Common forms of non-transitorycomputer-readable media include, for example, floppy disks, flexibledisks, hard disks, magnetic tape, or any other magnetic storage medium,CD-ROM, DVD, or any other optical medium, a RAM, a PROM, an EPROM, aFLASH-EPROM, or other memory chip or cartridge, or any other tangiblemedium from which a computer can read and use.

Alternatively, the method may be implemented in transitory media, suchas a transmittable carrier wave in which the control program is embodiedas a data signal using transmission media, such as acoustic or lightwaves, such as those generated during radio wave and infrared datacommunications, and the like.

It will be appreciated that variants of the above-disclosed and otherfeatures and functions, or alternatives thereof, may be combined intomany other different systems or applications. Various presentlyunforeseen or unanticipated alternatives, modifications, variations orimprovements therein may be subsequently made by those skilled in theart which are also intended to be encompassed by the following claims.

What is claimed is:
 1. A computer implemented method of detecting avehicle in a video frame, the video frame acquired from a fixed parkingoccupancy video camera including a field of view associated with avehicle parking region, the method comprising: a) capturing a videoframe from the fixed parking occupancy video camera, the video frameincluding a ROI (Region of Interest) oriented by an orientation anglerelative to an orientation of an image plane associated with thecaptured video frame, the ROI including one or more parking spaces ofthe vehicle parking region; b) rotating the ROI by the orientation angleof the ROI; and c) performing a sliding window-based 2-D (Dimensional)space search for a vehicle within the rotated ROI, the slidingwindow-based 2-D space search extracting one or more features associatedwith each of a plurality of windows and accessing a classifier toclassify each window as including a vehicle or not including a vehicle.2. The computer-implemented method of detecting a vehicle according toclaim 1, further comprising: d) suppressing one or more windowsincluding a common vehicle to eliminate overlapping windows detectingthe common vehicle.
 3. The computer implemented method of detecting avehicle according to claim 1, the method comprising: performing stepsa)-c) for a plurality of captured video frames.
 4. The computerimplemented method of detecting a vehicle according to claim 1, whereinthe ROI is defined by a longitudinal axis, and the orientation angle isdefined by the longitudinal axis of the ROI relative to the orientationof an axis associated with the image plane.
 5. The computer implementedmethod of detecting a vehicle according to claim 1, wherein step c)performs the sliding window-based 2-D space search along an x-axisassociated with the ROI, at various window widths, while the windowsmaintain a fixed height.
 6. The computer implemented method of detectinga vehicle according to claim 1, wherein step c) performs the slidingwindow-based 2-D space search along an x-axis associated with the ROI,at various window widths, while a height of the ROI changes relative toa street curb associated with the ROI.
 7. The computer implementedmethod of detecting a vehicle according to claim 1, wherein step c)performs a sliding window-based 3-D space search for a vehicle withinthe rotated ROI, the sliding window-based 3-D space search performingthe search along an x-axis associated with the ROI, at various windowwidths and various window heights.
 8. The computer implemented method ofdetecting a vehicle according to claim 1, wherein step b) rotates thecaptured video frame including the ROI, and the ROI is cropped from thecaptured video frame.
 9. The computer implemented method of detecting avehicle according to claim 1, wherein the ROI is defined as one of apolygon, an ellipse, and a parallelogram shape.
 10. The computerimplemented method of detecting a vehicle according to claim 1, whereinthe classifier is trained with video frames acquired from the fixedparking occupancy video camera.
 11. The computer implemented method ofdetecting a vehicle according to claim 10, wherein the classifier istrained with positive samples of video including a detected vehicle andnegative samples of video not including a detected vehicle.
 12. Thecomputer implemented method of detecting a vehicle according to claim11, wherein positive and negative samples are extracted from the rotatedROI for training the classifier.
 13. The computer implemented method ofdetecting a vehicle according to claim 1, wherein the parking regionincludes one or more of street parking, spot parking and parallelparking.
 14. A vehicle detection system associated with a vehicleparking region, the vehicle detection system comprising: a parkingoccupancy video camera directed towards the vehicle parking region; anda controller operatively associated with the parking occupancy videocamera, the controller configured to execute computer instructions toperform a process of detecting a vehicle in a video frame including: a)capturing a video frame from the parking occupancy video camera, thevideo frame including a ROI (Region of Interest) orientated by anorientation angle, relative to an orientation of an image planeassociated with the captured video frame, the ROI including one or moreparking spaces of the vehicle parking region; b) rotating the ROI by theorientation angle of the ROI; and c) performing a sliding window-based2-D (Dimensional) space search for a vehicle within the rotated ROI, thesliding window-based 2-D space search extracting one or more featuresassociated with each of a plurality of windows and accessing aclassifier to classify each window as including a vehicle or notincluding a vehicle.
 15. The vehicle detection system according to claim14, the process further comprising: d) suppressing one or more windowsincluding a common vehicle to eliminate overlapping windows detectingthe common vehicle.
 16. The vehicle detection system according to claim14, the process comprising: performing steps a)-c) for a plurality ofcaptured video frames.
 17. The vehicle detection system according toclaim 14, wherein the ROI is defined by a longitudinal axis, and theorientation angle is defined by the longitudinal axis of the ROIrelative to the orientation of an axis associated with the image plane.18. The vehicle detection system according to claim 14, wherein step c)performs the sliding window-based 2-D space search along an x-axisassociated with the ROI, at various window widths, while the windowsmaintain a fixed height.
 19. The vehicle detection system according toclaim 14, wherein step c) performs the sliding window-based 2-D spacesearch along an x-axis associated with the ROI, at various windowwidths, while a height of the ROI changes relative to a street curbassociated with the ROI.
 20. The vehicle detection system according toclaim 14, wherein step c) performs a sliding window-based 3-D spacesearch for a vehicle within the rotated ROI, the sliding window-based3-D space search performing along an x-axis associated with the ROI, atvarious window widths and various window heights.
 21. The vehicledetection system according to claim 14, wherein step b) rotates thecaptured video frame including the ROI, and the ROI is cropped from thecaptured video frame.
 22. The vehicle detection system according toclaim 14, wherein the ROI is defined as one of a polygon, an ellipse,and a parallelogram shape.
 23. The vehicle detection system according toclaim 14, wherein the classifier is trained with video frames acquiredfrom the fixed parking occupancy video camera.
 24. The vehicle detectionsystem according to claim 23, wherein the classifier is trained withpositive samples of video including a detected vehicle and negativesamples of video not including a detected vehicle.
 25. The vehicledetection system according to claim 14, wherein the parking regionincludes one or more of street parking, spot parking and parallelparking.
 26. A computer implemented method of determining parkingoccupancy associated with a vehicle parking region comprising: a)capturing a video frame from a parking occupancy video camera directedtowards the vehicle parking region; b) rotating the captured video frameby a predetermined orientation angle of a longitudinal axis associatedwith a ROI (Region of Interest) associated with the captured videoframe, relative to an orientation of an axis associated with an imageplane associated with the captured video frame, the ROI including one ormore parking spaces of the parking region; and c) performing a slidingwindow-based 2-D (Dimensional) space search for a vehicle within the ROIassociated with the rotated captured video frame, the slidingwindow-based 2-D space search extracting one or more features associatedwith each of a plurality of windows and accessing a classifier toclassify each window as including a vehicle or not including a vehicle.27. The computer implemented method of determining parking occupancyaccording to claim 26, wherein step c) performs non-maxima suppression.28. The computer implemented method of determining parking occupancyaccording to claim 26, wherein steps a)-c) are performed for a pluralityof captured video frames; and step c) performs the sliding window-based2-D space search along an x-axis associated with the ROI at variouswindow widths, and the classifier is trained with video frames acquiredfrom the fixed parking occupancy camera, the video frames includingpositive samples of video including a detected vehicle and negativesamples of video not including a detected vehicle.
 29. The computerimplemented method of determining parking occupancy according to claim28, wherein step c) extracts one or more feature vectors associated witheach of the plurality of windows, and the classifier is a SVM (SupportVector Machine).
 30. A computer implemented method of detecting avehicle in a video frame, the video frame acquired from a fixed parkingoccupancy video camera including a field of view associated with avehicle parking region, the method comprising: a) capturing a videoframe from the fixed parking occupancy video camera, the video frameincluding a ROI (Region of Interest) oriented by an orientation anglerelative to an orientation of an image plane associated with thecaptured video frame, the ROI including one or more parking spaces ofthe vehicle parking region; and b) performing a sliding window-based 2-D(Dimensional) Space Search for a vehicle with the ROI, the slidingwindow-based 2-D space search extracting one or more features associatedwith each of a plurality of windows and accessing a classifier toclassify each window as including a vehicle or not including a vehicle,wherein step b) performs the sliding window-based 2-D space search alongan orientation axis of the ROI associated with the orientation angle ofthe ROI relative to the orientation of the image plane.